[HTML][HTML] Time-series pattern recognition in Smart Manufacturing Systems: A literature review and ontology

MA Farahani, MR McCormick, R Gianinny… - Journal of Manufacturing …, 2023 - Elsevier
Since the inception of Industry 4.0 in 2012, emerging technologies have enabled the
acquisition of vast amounts of data from diverse sources such as machine tools, robust and …

Efficient convolutional neural networks for semiconductor wafer bin map classification

E Shin, CD Yoo - Sensors, 2023 - mdpi.com
The results obtained in the wafer test process are expressed as a wafer map and contain
important information indicating whether each chip on the wafer is functioning normally. The …

A deep residual neural network for semiconductor defect classification in imbalanced scanning electron microscope datasets

FL de la Rosa, JL Gómez-Sirvent, R Morales… - Applied Soft …, 2022 - Elsevier
The detection of defects using inspection systems is common in a wide range of
corporations such as semiconductor industries. The use of techniques based on deep …

Inpainted image reconstruction using an extended Hopfield neural network based machine learning system

W Citko, W Sienko - Sensors, 2022 - mdpi.com
This paper considers the use of a machine learning system for the reconstruction and
recognition of distorted or damaged patterns, in particular, images of faces partially covered …

Analysis of Image Hashing in Wafer Map Failure Pattern Recognition

M Piao, CH Jin - IEEE Transactions on Semiconductor …, 2023 - ieeexplore.ieee.org
There are mainly two types of wafer map failure pattern recognition, ie, traditional
classification based and deep learning based approaches. Traditional classification usually …

Antecedent hash modality learning and representation for enhanced wafer map defect pattern recognition

M Piao, CH Jin, B Zhong - Expert Systems with Applications, 2024 - Elsevier
In wafer map defect pattern recognition, deep learning methods are predominantly used.
These models autonomously learn features without explicit human intervention due to their …

Tree species identification in urban environments using TensorFlow lite and a transfer learning approach

D Pacheco-Prado, E Bravo-López, LÁ Ruiz - Forests, 2023 - mdpi.com
Building and updating tree inventories is a challenging task for city administrators, requiring
significant costs and the expertise of tree identification specialists. In Ecuador, only the Trees …

Wafer Map Defect Pattern Recognition using Imbalanced Datasets

T Tziolas, T Theodosiou… - … & Applications (IISA), 2022 - ieeexplore.ieee.org
The accurate and automatic inspection of wafer maps is vital for semiconductor engineers to
identify defect causes and to optimize the wafer fabrication process. This research work …

No-Reference image quality assessment based on image multi-scale contour prediction

F Wang, J Chen, H Zhong, Y Ai, W Zhang - Applied Sciences, 2022 - mdpi.com
Accurately assessing image quality is a challenging task, especially without a reference
image. Currently, most of the no-reference image quality assessment methods still require …

Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps

DA Altantawy, MA Yakout - Journal of Intelligent Manufacturing, 2024 - Springer
Due to the complexity and dynamics of the semiconductor manufacturing processes, wafer
bin maps (WBM) present various defect patterns caused by various process faults. The …